Traffic Control Strategy Formulation and Optimization Enabled by Homogenous Connected and Autonomous Vehicle Systems.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

An Optimization Approach for Energy Efficient Coordination Control of Vehicles in Merging Highways

Environmental concerns along with stronger governmental regulations regarding automotive fuel-economy and greenhouse-gas emissions are contributing to the push for development of more sustainable transportation technologies. Furthermore, the widespread use of the automobile gives rise to other issues such as traffic congestion and increasing traffic accidents. Consequently, two main goals of new technologies are the reduction of vehicle fuel consumption and emissions and the reduction of traffic congestion. While an extensive list of published work addresses the problem of fuel consumption reduction by optimizing the vehicle powertrain operations, particularly in the case of hybrid electric vehicles (HEV), approaches like eco-driving and traffic coordination have been studied more recently as alternative methods that can, in addition, address the problem of traffic congestion and traffic accidents reduction. This dissertation builds on some of those approaches, with particular emphasis on autonomous vehicle coordination control. In this direction, the objective is to derive an optimization approach for energy efficient and safe coordination control of vehicles in merging highways. Most of the current optimization-based centralized approaches to this problem are solved numerically, at the expense of a high computational load which limits their potential for real-time implementation. In addition, closed-form solutions, which are desired to facilitate traffic analysis and the development of approaches to address interconnected merging/intersection points and achieve further traffic improvements at the road-network level, are very limited in the literature. In this dissertation, through the application of the Pontryagin’s minimum principle, a closed-form solution is obtained which allows the implementation of a real-time centralized optimal control for fleets of vehicles. The results of applying the proposed framework show that the system can reduce the fuel consumption by up to 50% and the travel time by an average of 6.9% with respect to a scenario with not coordination strategy. By integrating the traffic coordination scheme with in-vehicle energy management, a two level optimization system is achieved which allows assessing the benefits of integrating hybrid electric vehicles into the road network. Regarding in-vehicle energy optimization, four methods are developed to improve the tuning process of the equivalent consumption optimization strategy (ECMS). First, two model predictive control (MPC)-based strategies are implemented and the results show improvements in the efficiency obtained with the standard ECMS implementation. On the other hand, the research efforts focus in performing analysis of the engine and electric motor operating points which can lead to the optimal tuning of the ECMS with reduced iterations. Two approaches are evaluated and even though the results in fuel economy are slightly worse than those for the standard ECMS, they show potential to significantly reduce the tuning time of the ECMS. Additionally, the benefits of having less aggressive driving profiles on different powertrain technologies such as conventional, plug-in hybrid and electric vehicles are studied

Transit Priority

69A3551947136, #79070-16 #79070-17To establish an efficient way of improving the service quality of the public transportation system, this research seeks the optimal vehicle scheduling at a multi-conflict area considering heterogeneous vehicle headways and weighted by vehicle occupancies to minimize the total travel time delay cost while giving priority to transits with higher occupancy. A mixed-integer programming (MIP) model is proposed to solve the exact optimal solution to this problem and a customized branch-and-bound algorithm is designed to improve computational efficiency. A set of numerical experiments in various scenarios are tested to demonstrate the feasibility and effectiveness of the proposed model and algorithm. The comparison results show that coordination of vehicles with individual-vehicle-based control can significantly increase the capacity of the conflict area and reduce the delay of transits compared to existing well-known control strategies (e.g., stop signs and signals)

The Hierarchical Control Method for Coordinating a Group of Connected Vehicles on Urban Roads

Safety, mobility and environmental impact are the three major challenges in today\u27s transportation system. As the advances in wireless communication and vehicle automation technologies, they have rapidly led to the emergence and development of connected and automated vehicles (CAVs). We can expect fully CAVs by 2030. The CAV technologies offer another solution for the issues we are dealing with in the current transportation system. In the meanwhile, urban roads are one of the most important part in the transportation network. Urban roads are characterized by multiple interconnected intersections. They are more complicated than highway traffic, because the vehicles on the urban roads are moving in multiple directions with higher relative velocity. Most of the traffic accidents happened at intersections and the intersections are the major contribution to the traffic congestions. Our urban road infrastructures are also becoming more intelligent. Sensor-embedded roadways are continuously gathering traffic data from passing vehicles. Our smart vehicles are meeting intelligent roads. However, we have not taken the fully advantages of the data rich traffic environment provided by the connected vehicle technologies and intelligent road infrastructures. The objective of this research is to develop a coordination control strategy for a group of connected vehicles under intelligent traffic environment, which can guide the vehicles passing through the intersections and make smart lane change decisions with the objective of improving overall fuel economy and traffic mobility. The coordination control strategy should also be robust to imperfect connectivity conditions with various connected vehicle penetration rate. This dissertation proposes a hierarchical control method to coordinate a group of connected vehicles travelling on urban roads with intersections. The dissertation includes four parts of the application of our proposed method: First, we focus on the coordination of the connected vehicles on the multiple interconnected unsignalized intersection roads, where the traffic signals are removed and the collision avoidance at the intersection area relays on the communication and cooperation of the connected vehicles and intersection controllers. Second, a fuel efficient hierarchical control method is proposed to control the connected vehicles travel on the signalized intersection roads. With the signal phase and timing (SPAT) information, our proposed approach is able to help the connected vehicles minimize red light idling and improve the fuel economy at the same time. Third, the research is extended form single lane to multiple lane, where the connected vehicle discretionary and cooperative mandatory lane change have been explored. Finally, we have analysis the real-world implementation potential of our proposed algorithm including the communication delay and real-time implementation analysis

Scalable Map Information Dissemination for Connected and Automated Vehicle Systems

Situational awareness in connected and automated vehicle (CAV) systems becomes particularly challenging in the presence of non-line of sight objects and/or objects beyond the sensing range of local onboard sensors. Despite the fact that fully autonomous driving requires the use of multiple redundant sensor systems, primarily including camera, radar, and LiDAR, the non-line of sight object detection problem still persists due to the inherent limitations of those sensing techniques. To tackle this challenge, the inter-vehicle communication system is envisioned that allows vehicles to exchange self-status updates aiming to extend their effective field of view and thus compensate for the limitations of the vehicle tracking subsystem that relies substantially on onboard sensing devices. Tracking capability in such systems can be further improved through the cooperative sharing of locally created map data instead of transmitting only self-update messages containing core basic safety message (BSM) data. In the cooperative sharing of safety messages, it is imperative to have a scalable communication protocol to ensure optimal use of the communication channel. This dissertation contributes to the analysis of the scalability issue in vehicle-to-everything (V2X) communication and then addresses the range issue of situational awareness in CAV systems by proposing a content-adaptive V2X communication architecture. To that end, we first analyze the BSM scheduling protocol standardized in the SAE J2945/1 and present large-scale scalability results obtained from a high-fidelity simulation platform to demonstrate the protocol\u27s efficacy to address the scalability issues in V2X communication. By employing a distributed opportunistic approach, the SAE J2945/1 congestion control algorithm keeps the overall offered channel load within an optimal operating range, while meeting the minimum tracking requirements set forth by upper-layer applications. This scheduling protocol allows event-triggered and vehicle-dynamics driven message transmits that further the situational awareness in a cooperative V2X context. Presented validation results of the congestion control algorithm include position tracking errors as the performance measure, with the age of communicated information as the evaluation measure. In addition, we examine the optimality of the default settings of the congestion control parameters. Comprehensive analysis and trade-off study of the control parameters reveal some areas of improvement to further the algorithm\u27s efficacy. Motivated by the effectiveness of channel congestion control mechanism, we further investigate message content and length adaptations, together with transmit rate control. Reasonably, the content of the exchanged information has a significant impact on the map accuracy in cooperative driving systems. We investigate different content control schemes for a communication architecture aimed at map sharing and evaluate their performance in terms of position tracking error. This dissertation determines that message content should be concentrated to mapped objects that are located farther away from the sender to the edge of the local sensor range. This dissertation also finds that optimized combination of message length and transmit rate ensures the optimal channel utilization for cooperative vehicular communication, which in turn improves the situational awareness of the whole system

Cooperative Traffic Control Framework for Mixed Vehicular Flows

A prompt revolution is foreseen in the transportation sector, when the current conventional human-driven vehicles will be replaced by fully connected and automated vehicles. As a result, there will be a transition period where both types will coexist until the later type is fully adopted in the traffic networks. This new mix of traffic flow on the existing transportation network will require developing a new ecosystem able to accommodate both types of vehicles in traffic network environments of the future. A major challenging issue related to the emerging mixed transportation ecosystem is the lack of an adequate model and control framework. This is especially important for modeling traffic safety and operations at network bottlenecks such as highway merging areas. Therefore, the main goal of this thesis is to develop a microscopic modeling and hierarchical cooperative control framework specifically for mixed traffic at highway on-ramps. In this thesis, a two-level hierarchical traffic control framework is proposed for mixed traffic at highway merging areas. In this regard, for the lower level of the proposed framework, this thesis establishes a set of fundamental trajectory-based cooperative control algorithms for different merging scenarios under mixed traffic conditions. We identify six scenarios, consisting of triplets of vehicles, defined based on the different combinations of CAVs and conventional vehicles. For each triplet, different consecutive movement phases along with corresponding desired distance and velocity set-points are defined. Via the movement phases, the CAVs engaged in each triplet cooperate to calculate their optimal-smooth trajectories aiming at facilitating the merging maneuver while complying with the realistic constraints related to the safety and comfort of vehicle occupants. The vehicles in each triplet are modeled by a distinct system, and a Model Predictive Control scheme is employed to calculate the cooperative optimal control inputs (acceleration values) for CAVs, accounting for conventional vehicles’ uncertainties. In the next step of the thesis, for the higher level of the proposed framework, a merging sequence determination and triplets’ formation methodology is developed based on predicting the arrival time of vehicles into the merging area and according to the priority in choosing different triplet types. To model the merging maneuvers when two consecutive triplets share a vehicle, the interactions between triplets of vehicles are also investigated. In order to develop a microscopic traffic simulator, we analytically formulate different vehicles’ driving behaviors under cooperative (i.e., the proposed traffic control framework) and non-cooperative (i.e., normal) operation modes and discuss the switching conditions between these driving modes. To evaluate the effectiveness of the proposed framework, first, each triplet is simulated in MATLAB and evaluated for different sets of system initial values. Without a need for readjusting the algorithm for different initial values, the simulation results show that the proposed cooperative merging algorithms ensure smooth merging maneuvers while satisfying all the prescribed constraints, e.g., speed limits, safe distances, and comfortable acceleration and jerk values. Moreover, a simulator is developed in MATLAB for the entire framework (including both the higher and lower level of the framework) to evaluate the impact of all the triplets on continuous mixed traffic flow. Different penetration rates of CAVs under different traffic flow conditions are evaluated through the developed simulator. The simulation results show that the proposed cooperative methodology, comparing to the non-cooperative operation, can improve the average travel time of merging vehicles without disturbing the mainstream flow, provide safer merging maneuvers by avoiding the merging vehicles to stop at the end of the acceleration lane, and guarantee smooth motion trajectories for CAVs (i.e., derivable position and speed along with limited changes in acceleration values). Generally, the results emphasize that the proposed cooperative traffic control framework can improve the mixed traffic conditions in terms of both traffic safety and operations. Moreover, the simulator provides a tool for the transportation community to evaluate their existing infrastructures under different penetration rates of CAVs and examine different traffic control plans for a mixed traffic environment. As the merging maneuver is only one application of gap-acceptance models, other types of maneuvers (e.g., lane changing, vehicle turning, etc.) can be similarly modelled. Thus, we can extend the proposed framework to the multi-lane highways, roundabouts, and urban area intersections. Furthermore, the arrival time prediction of the vehicles can be improved to elevate the performance of the proposed framework during the very congested traffic conditions

Safe and Secure Control of Connected and Automated Vehicles

Evolution of Connected and Automated Vehicles (CAV), as an important class of Cyber-Physical Systems (CPS), plays a crucial role in providing innovative services in transport and traffic management. Vehicle platoons, as a set of CAV, forming a string of connected vehicles, have offered significant enhancements in traffic management, energy consumption, and safety in intelligent transportation systems. However, due to the existence of the cyber layer in these systems, subtle security related issues have been underlined and need to be taken into account with sufficient attention. In fact, despite the benefits brought by the platoons, they potentially suffer from insecure networks which provide the connectivity among the vehicles participating in the platoon which makes these systems prone to be under the risk of cyber attacks. One (or more) external intelligent intruder(s) might attack one (or more) of the vehicles participating in a platoon. In this respect, the need for a safe and secure driving experience is highly sensible and crucial. Hence, we will concentrate on improving the safety and security of CAVs in different scenarios by taking advantage of security related approaches and CAV control systems. In this thesis, we are going to focus on two main levels of platoon control, namely I) High level secure platoon control, and II) Low level secure platoon control. In particular, in the high level part, we consider platoons with arbitrary inter-vehicular communication topoloy whereby the vehicles are able to exchange their driving data with each other through DSRC-based environment. The whole platoon is modeled using graph-theoretic notions by denoting the vehicles as the nodes and the inter-vehicular communication quality as the edge weights. We study the security of the vehicle platoon exposed to cyber attacks using a novel game-theoretic approach. The platoon topologies under investigation are directed (called predecessor following) or undirected (bidirectional) weighted graphs. The attacker-detector game is defined as follows. The attacker targets some vehicles in the platoon to attack and the detector deploys monitoring sensors on the vehicles. The attacker's objective is to be as stealthy to the sensors as possible while the detector tries to place the monitoring sensors to detect the attack impact as much as he can. The existence of equilibrium strategies for this game is investigated based on which the detector can choose specific vehicles to put his sensors on and increase the security level of the system. Moreover, we study the effect of adding (or removing) communication links between vehicles on the game value. We then address the same problem while investigating the optimal actuator placement strategy needed by the defender to mitigate the effects of the attack. In this respect, the energy needed by the attacker to steer the consensus follower-leader dynamics of the system towards his desired direction is used as the game payoff. Simulation and experimental results conducted on a vehicle platoon setup using Robotic Operating System (ROS) demonstrate the effectiveness of our analyses. In the low level platoon control, we exploit novel secure model predictive controller algorithms to provide suitable countermeasure against a prevalent data availability attack, namely Denial-of-Service (DoS) attack. A DoS intruder can endanger the security of platoon by jamming the communication network among the vehicles which is responsible to transmit inter-vehicular data throughout the platoon. In other words, he may cause a failure in the network by jamming it or injecting a huge amount of delay, which in essence makes the outdated transferred data useless. This can potentially result in huge performance degradation or even hazardous collisions. We propose novel secure distributed nonlinear model predictive control algorithms for both static and dynamic nonlinear heterogeneous platoons which are capable of handling DoS attack performed on a platoon equipped by different communication topologies and at the same time they guarantee the desired formation control performance. Notably, in the dynamic case, our proposed method is capable of providing safe and secure control of the platoon in which arbitrary vehicles might perform cut-in and/or cut-out maneuvers. Convergence time analysis of the system are also investigated. Simulation results on a sample heterogeneous attacked platoon exploiting two-predecessor follower communication environment demonstrates the fruitfulness of the method